Multispecies collective waving behaviour in fish
Bibliographic record
Abstract
Abstract Collective behaviour is widely accepted to provide a variety of antipredator benefits. Acting collectively requires not only strong coordination among group members, but also the integration of among-individual phenotypic variation. Therefore, groups composed of more than one species offer a unique opportunity to look into the evolution of both mechanistic and functional aspects of collective behaviour. Here, we present data on mixed-species fish shoals that perform collective dives. These repeated dives produce water waves capable of delaying and/or reducing the success of piscivorous bird attacks. The large majority of the fish in these shoals consist of the sulphur molly, Poecilia sulphuraria, but we regularly also found a second species, the widemouth gambusia, Gambusia eurystoma, making these shoals mixed-species aggregations. In a set of laboratory experiments, we found that gambusia were much less inclined to dive after an attack as compared with mollies, which almost always dive, though mollies dived less deep when paired with gambusia that did not dive. By contrast, the behaviour of gambusia was not influenced by the presence of diving mollies. The dampening effect of less responsive gambusia on molly diving behaviour can have strong evolutionary consequences on the overall collective waving behaviour as we expect shoals with a high proportion of unresponsive gambusia to be less effective at producing repeated waves. This article is part of a discussion meeting issue ‘Collective behaviour through time’.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".